Instructions to use naver-hyperclovax/HyperCLOVAX-SEED-Think-14B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use naver-hyperclovax/HyperCLOVAX-SEED-Think-14B with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="naver-hyperclovax/HyperCLOVAX-SEED-Think-14B", trust_remote_code=True)
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("naver-hyperclovax/HyperCLOVAX-SEED-Think-14B", trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained("naver-hyperclovax/HyperCLOVAX-SEED-Think-14B", trust_remote_code=True)
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use naver-hyperclovax/HyperCLOVAX-SEED-Think-14B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "naver-hyperclovax/HyperCLOVAX-SEED-Think-14B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "naver-hyperclovax/HyperCLOVAX-SEED-Think-14B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/naver-hyperclovax/HyperCLOVAX-SEED-Think-14B

SGLang

How to use naver-hyperclovax/HyperCLOVAX-SEED-Think-14B with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "naver-hyperclovax/HyperCLOVAX-SEED-Think-14B" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "naver-hyperclovax/HyperCLOVAX-SEED-Think-14B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "naver-hyperclovax/HyperCLOVAX-SEED-Think-14B" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "naver-hyperclovax/HyperCLOVAX-SEED-Think-14B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use naver-hyperclovax/HyperCLOVAX-SEED-Think-14B with Docker Model Runner:
```
docker model run hf.co/naver-hyperclovax/HyperCLOVAX-SEED-Think-14B
```

kbu1564 commited on Jul 21, 2025

Commit

bc3c28a

verified ·

1 Parent(s): 64e5736

Update README.md

Browse files

Files changed (1) hide show

README.md +2 -0

README.md CHANGED Viewed

@@ -5,6 +5,8 @@ license_link: LICENSE
 library_name: transformers
 ---
 ## Overview
 HyperCLOVA X SEED Think 14B is a next-generation language model that moves beyond the conventional approach of simply increasing model size to improve performance. It combines [HyperCLOVA X’s lightweighting technology](https://clova.ai/tech-blog/%EC%9E%91%EC%A7%80%EB%A7%8C-%EA%B0%95%EB%A0%A5%ED%95%98%EA%B2%8C-%EA%B3%A0%ED%9A%A8%EC%9C%A8-llm%EC%9D%84-%EB%A7%8C%EB%93%9C%EB%8A%94-hyperclova-x%EC%9D%98-%EA%B2%BD%EB%9F%89%ED%99%94-%EA%B8%B0) for building high-efficiency LLMs with advanced reasoning capabilities. Its development relied on two key technologies: (1) Pruning & Knowledge Distillation, which achieves both compactness and high performance, and (2) a Reinforcement Learning (RL) pipeline, which maximizes reasoning ability. By pruning low-importance parameters and distilling knowledge from a large model into a smaller one, training costs have been significantly reduced. On top of this, [the latest RL recipe validated in HyperCLOVA X Think](https://arxiv.org/pdf/2506.22403) is applied in a multi-stage process: (1) Supervised Fine-Tuning (SFT), (2) Reinforcement Learning with Verifiable Rewards (RLVR), (3) Length Controllability (LC) for reasoning path optimization, and (4) a joint training of Reinforcement Learning from Human Feedback (RLHF) and RLVR.

 library_name: transformers
 ---
+![image/png](https://cdn-uploads.huggingface.co/production/uploads/6512d9827fccffe1e9e28fa7/7BT1W9eHLQjRCCENwcXmE.png)
 ## Overview
 HyperCLOVA X SEED Think 14B is a next-generation language model that moves beyond the conventional approach of simply increasing model size to improve performance. It combines [HyperCLOVA X’s lightweighting technology](https://clova.ai/tech-blog/%EC%9E%91%EC%A7%80%EB%A7%8C-%EA%B0%95%EB%A0%A5%ED%95%98%EA%B2%8C-%EA%B3%A0%ED%9A%A8%EC%9C%A8-llm%EC%9D%84-%EB%A7%8C%EB%93%9C%EB%8A%94-hyperclova-x%EC%9D%98-%EA%B2%BD%EB%9F%89%ED%99%94-%EA%B8%B0) for building high-efficiency LLMs with advanced reasoning capabilities. Its development relied on two key technologies: (1) Pruning & Knowledge Distillation, which achieves both compactness and high performance, and (2) a Reinforcement Learning (RL) pipeline, which maximizes reasoning ability. By pruning low-importance parameters and distilling knowledge from a large model into a smaller one, training costs have been significantly reduced. On top of this, [the latest RL recipe validated in HyperCLOVA X Think](https://arxiv.org/pdf/2506.22403) is applied in a multi-stage process: (1) Supervised Fine-Tuning (SFT), (2) Reinforcement Learning with Verifiable Rewards (RLVR), (3) Length Controllability (LC) for reasoning path optimization, and (4) a joint training of Reinforcement Learning from Human Feedback (RLHF) and RLVR.